Open AccessMachine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review
Author(s) -
Xiao Zhang,
Boyang Zhao,
Yun Lin
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3128669
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The most important parts of rotating machinery are the rolling bearings. Finding bearing faults in time can avoid affecting the operation of the entire equipment. The data-driven fault diagnosis technology of bearings has recently become a research hotspot, and the starting point of research is often the acquisition of vibration signals. There are many public data sets for rolling bearings. Among them, the most widely used public dataset is Case Western Reserve University bearing center (CWRU). This paper will start from the CWRU data set, compare and analyze some basic methods of machine learning based rolling bearing fault diagnosis, and summarize the characteristics of CWRU. First, we give a comprehensive introduction to CWRU and summarize the results achieved. After that, the basic methods and principles of machine learning based rolling bearing fault diagnosis were summarized. Finally, we conduct experiments and analyze experimental results. This paper will have certain guiding significance for the future use of CWRU for machine learning based rolling bearing fault diagnosis.